Phase_Unwrapping_2D

multiple FDF file reader build on the Sharon Zhang FDF reader.

matlab中GUI关于信号与系统的设计(matlab in the GUI design of signals and systems)

matlab7.0，适合初学者，不错的基础教程。。(Matlab tutorial foundation )

沿直线最小化优化学习率的梯度下降法matlab版本的一个实例源代码，带轨迹收敛图。(a source code of steepest descent with learning rate to minimize along the line by matlab)

将OFDM技术运用在可见光通信的研究，并且把OFDM的高峰均比这一缺点进行了建设性的运用。(In particular, the use of OFDM (orthogonal frequency division multiplexing) for intensity modulation is investigated. The high peak-to-average ratio (PAR) in OFDM is usually considered a disadvantage in radio frequency transmission systems due to non-linearities of the power amplifier. It is demonstrated theoretically and by means of an experimental system that the high PAR in OFDM can be exploited constructively in visible light communication to intensity modulate LEDs. It is shown that the theoretical and the experimental results match very closely, and that it is possible to cover a distance of up to one meter using a single LED.)

Polar code with CA-SCL Decoder

说明：  MATLAB程序（附图像），利用彩色AGV对路径进行识别。(MATLAB (AP like), using color AGV right path for identification.)

src 算法中的迭代算法,通过该算法可以计算SRC的参数，通过使用线性表示来求。(src algorithm iterative algorithm, the algorithm can be calculated by the SRC parameter, by using a linear representation requirements.)

聚类算法，不是分类算法。分类算法是给一个数据，然后判断这个数据属于已分好的类中的具体哪一类。聚类算法是给一大堆原始数据，然后通过算法将其中具有相似特征的数据聚为一类。这里的k-means聚类，是事先给出原始数据所含的类数，然后将含有相似特征的数据聚为一个类中。所有资料中还是Andrew Ng介绍的明白。首先给出原始数据{x1,x2,...,xn}，这些数据没有被标记的。初始化k个随机数据u1,u2,...,uk。这些xn和uk都是向量。根据下面两个公式迭代就能求出最终所有的u,这些u就是最终所有类的中心位置。(Clustering algorithm, not a classification algorithm. Classification algorithm is to give a figure, and then determine the data belonging to a specific class of good which category. Clustering algorithm is to give a lot of raw data, and then through the algorithm which has similar characteristics data together as a class. Here k-means clustering, is given in advance the number of classes contained in the raw data, then the data contain similar characteristics together as a class. All information presented in or Andrew Ng understand. Firstly, raw data {x1, x2, ..., xn}, the data is not labeled. K random initialization data u1, u2, ..., uk. These are the vectors xn and uk. According to the following two formulas can be obtained final iteration all u, u is the ultimate all these classes the center position.)

采用的是通用的平面波展开法,旋转机械二维全息谱计算，含噪脉冲信号进行相关检测。( Using common plane wave expansion method, Rotating machinery 2-d holographic spectrum calculation, Noisy pulse correlation detection signal.)